1. Field of the Invention
The present invention relates to a method and system for whole picture image processing and, more particularly, a method and system for automatic texture and color analysis of images in accordance with rapid, whole picture processing for the purpose of achieving automatic classification of the images in accordance with certain predetermined standards.
2. Description of the Prior Art
Radiography has for many years been an essential tool in the fields of diagnostic and preventive medicine, and industrial quality control, among others.
With the advent of television, systems and methods of examining X-ray pictures by the use of closed-circuit networks were incorporated into medical X-ray examination procedures. Both single-camera and dual-camera systems, as well as various analog circuitry arrangements for X-ray image analysis, were developed. See, for example, U.S. Pat. No. 3,283,071--Rose et al.
Additionally, various digital systems were utilized, in conjunction with closed-circuit television networks, to convert radiographic images produced by an irradiated test object and a corresponding radiographic image produced by an irradiated reference object into corresponding video signals, which were then processed (such as, by comparator circuitry) so as to be electronically interpreted without human intervention. See, for example, U.S. Pat. No. 3,580,997--Webb et al.
In more recent years, sophisticated automatic pattern recognition procedures and algorithms have been developed, and this development has facilitated the analysis and processing of images (such as X-rays) by conventional digital data processing techniques and systems. However, many of the most important potential applications of computer pattern recognition, particularly in the field of clinical medicine, have not yet been successfully carried out on a feasible basis because of one fundamental difficulty.
The applications, such as the analysis of chest X-rays, Papanicolaou smears, differential white blood cell counting, and so forth, require an evaluation of the "texture" of the objects in the picture as an essential parameter for successful pattern recognition. The techniques presently used--the classical curvilinear boundary analysis techniques, counting algorithms, and gray-level histograms--have proved to be inadequate because these methods are unable to recognize the texture variations that are an essential characteristic feature of biomedical picture data. Thus, the indentification and quantification of the nature and extent of lung opacities, cytoplasmic and nuclear granularity, etc. (which are vital to clinical diagnosis and biomedical research) require new methods for evaluating the texture of areas of the picture.
The texture of a picture (or a scene) is characterized by many repetitive variations of subpatterns within the overall pattern, according to well-defined placement rules. The subpatterns can be spatial variations in intensity and/or wave length. Texture analysis by a digital computer involves a type of computing that is presently extremely time consuming and therefore very expensive. This is due to the fact that texture analysis involves the comparison of each picture point (or a selected collection of points) with every other point (or every point in some neighborhood around the selected points) of the picture. Since a digital computer can work with only one point at a time (or, at most, only a few points that can be packed into a single computer word), the number of instructions that must be executed is some multiple of KN, where N is the number of points in the picture and K is related to the size of the neighborhood that will be involved around each point.
For example, suppose the picture to be analyzed for texture has 500 lines and 500 picture points per line, thus making N=250,000, and the reasonable neighborhood size is 20 points .times.20 points, making K=400. If picture analysis is performed by digital data processing, and if 10 assembly-language instructions are required per neighborhood operation, then 250,000.times.400.times.10=10.sup.9 instructions must be executed for each complete picture-texture operation. Assuming that the computer executes 10.sup.6 instructions per second, each texture-type operation would take 10.sup.9 /10.sup.6 =10.sup.3 seconds or 16.7 minutes. Thus, the cost of executing a reasonably useful algorithm would be quite astronomical.
In conventional computer systems for processing image data, system memory is typically broken down into bits, bytes and words, and each individual byte or word has a unique address. Accordingly, the steps of accessing a word from memory, performing the required operation, and storing the result back into memory consume an inordinate amount of time when an entire picture operation is being executed by such a system.
Furthermore, in conventional systems, visual display of results is not available without reading the picture back from the memory, point by point, and converting it into analog form for TV display.
Whereas conventional systems are capable of accomplishing texture analysis--even though in a time-consuming and inefficient manner--colored data developed during image detection procedures can only be analyzed in a qualititive way, there being presently available no method and system for analyzing such colored data both quantitatively and rapidly.